Magnetic biosensors for identification of SARS-CoV-2, Influenza, HIV, and Ebola viruses: a review.

Infectious diseases such as novel coronavirus (SARS-CoV-2), Influenza, HIV, Ebola, etc kill many people around the world every year (SARS-CoV-2 in 2019, Ebola in 2013, HIV in 1980, Influenza in 1918). For example, SARS-CoV-2 has plagued higher than 317 000 000 people around the world from December 2019 to January 13, 2022. Some infectious diseases do not yet have not a proper vaccine, drug, therapeutic, and/or detection method, which makes rapid identification and definitive treatments the main challenges. Different device techniques have been used to detect infectious diseases. However, in recent years, magnetic materials have emerged as active sensors/biosensors for detecting viral, bacterial, and plasmids agents. In this review, the recent applications of magnetic materials in biosensors for infectious viruses detection have been discussed. Also, this work addresses the future trends and perspectives of magnetic biosensors.

Epidemic size, trend and spatiotemporal mapping of SARS-CoV-2 using geographical information system in Alborz Province, Iran.

BACKGROUND: The first confirmed cases of COVID-19 in Iran were reported in Qom city. Subsequently, the neighboring provinces and gradually all 31 provinces of Iran were involved. This study aimed to investigate the case fatility rate, basic reproductive number in different period of epidemic, projection of daily and cumulative incidence cases and also spatiotemporal mapping of SARS-CoV-2 in Alborz province, Iran. METHODS: A confirmed case of COVID-19 infection was defined as a case with a positive result of viral nucleic acid testing in respiratory specimens. Serial interval (SI) was fitted by gamma distribution and considered the likelihood-based R0 using a branching process with Poisson likelihood. Seven days average of cases, deaths, doubling times and CFRs used to draw smooth charts. kernel density tool in Arc GIS (Esri) software has been employed to compute hot spot area of the study site. RESULTS: The maximum-likelihood value of R0 was 2.88 (95%, CI: 2.57-3.23) in the early 14 days of epidemic. The case fatility rate for Alborz province (Iran) on March 10, was 8.33% (95%, CI:6.3-11), and by April 20, it had an increasing trend and reached 12.9% (95%,CI:11.5-14.4). The doubling time has been increasing from about two days and then reached about 97 days on April 20, 2020, which shows the slowdown in the spread rate of the disease. Also, from March 26 to April 2, 2020 the whole Geographical area of Karj city was almost affected by SARS-CoV-2. CONCLUSIONS: The R0 of COVID-19 in Alborz province was substantially high at the beginning of the epidemic, but with preventive measures and public education and GIS based monitoring of the cases,it has been reduced to 1.19 within two months. This reduction highpoints the attainment of preventive measures in place, however we must be ready for any second epidemic waves during the next months.

Risk and predictors of in-hospital mortality from COVID-19 in patients with diabetes and cardiovascular disease.

BACKGROUND: Diabetes mellitus (DM) and cardiovascular disease (CVD) are present in a large number of patients with novel Coronavirus disease 2019 (COVID-19). We aimed to determine the risk and predictors of in-hospital mortality from COVID-19 in patients with DM and CVD. METHODS: This retrospective cohort study included hospitalized patients aged ≥ 18 years with confirmed COVID-19 in Alborz province, Iran, from 20 February 2020 to 25 March 2020. Data on demographic, clinical and outcome (in-hospital mortality) data were obtained from electronic medical records. Self-reported comorbidities were classified into the following groups: "DM" (having DM with or without other comorbidities), "only DM" (having DM without other comorbidities), "CVD" (having CVD with or without other comorbidities), "only CVD" (having CVD without other comorbidities), and "having any comorbidity". Multivariate logistic regression models were fitted to quantify the risk and predictors of in-hospital mortality from COVID-19 in patients with these comorbidities. RESULTS: Among 2957 patients with COVID-19, 2656 were discharged as cured, and 301 died. In multivariate model, DM (OR: 1.62 (95% CI 1.14-2.30)) and only DM (1.69 (1.05-2.74)) increased the risk of death from COVID-19; but, both CVD and only CVD showed non-significant associations (p > 0.05). Moreover, "having any comorbidities" increased the risk of in-hospital mortality from COVID-19 (OR: 2.66 (95% CI 2.09-3.40)). Significant predictors of mortality from COVID-19 in patients with DM were lymphocyte count, creatinine and C-reactive protein (CRP) level (all P-values < 0.05). CONCLUSIONS: Our findings suggest that diabetic patients have an increased risk of in-hospital mortality following COVID-19; also, lymphocyte count, creatinine and CRP concentrations could be considered as significant predictors for the death of COVID-19 in these patients.